Indigenous Cultural Safety Training for Applied Health, Social Work, and Education Professionals: A PRISMA Scoping Review
Bibliographic record
Abstract
Anti-Indigenous racism is a widespread social problem in health and education systems in English-speaking colonized countries. Cultural safety training (CST) is often promoted as a key strategy to address this problem, yet little evidence exists on how CST is operationalized and evaluated in health and education systems. This scoping review sought to broadly synthesize the academic literature on how CST programs are developed, implemented, and evaluated in the applied health, social work and education fields in Canada, United States, Australia, and New Zealand. MEDLINE, EMBASE, CINAHL, ERIC, and ASSIA were searched for articles published between 1996 and 2020. The Joanna Briggs Institute's three-step search strategy and PRISMA extension for scoping reviews were adopted, with 134 articles included. CST programs have grown significantly in the health, social work, and education fields in the last three decades, and they vary significantly in their objectives, modalities, timelines, and how they are evaluated. The involvement of Indigenous peoples in CST programs is common, but their roles are rarely specified. Indigenous groups must be intentionally and meaningfully engaged throughout the entire duration of research and practice. Cultural safety and various related concepts should be careful considered and applied for the relevant context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.064 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.026 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".